Dynamic Modeling and Prediction Method of Reservoir Production Based on Multi-Parameter Time Series and Particle Swarm Optimization Algorithm

This study proposes a dynamic modeling and prediction method for reservoir production based on multi parameter time series and particle swarm optimization algorithm (PSO), aiming to improve the accuracy and timeliness of reservoir production prediction. This method integrates multi parameter time series data from the production process of oil reservoirs, such as oil well production, pressure, temperature, etc. It uses Long Short Term Memory (LSTM) neural networks to capture the dynamic changes of oil reservoirs, and combines PSO algorithm to optimize the LSTM model parameters to improve prediction accuracy. A reservoir production prediction model that can be dynamically updated with time is studied and constructed. This model can retrain the LSTM model with newly collected production data and re-optimize the model parameters to adapt to the changes in the reservoir production process. The empirical analysis shows that the prediction accuracy of the model on the test set is high, which can effectively capture the changing trend of reservoir production and provide important reference for oilfield operation. In addition, the model has the ability of real-time training and automatic updating, which ensures the timeliness of prediction. By accurately predicting reservoir production, the model is helpful for oilfield enterprises to arrange production plans reasonably, optimize resource allocation, reduce production costs and improve overall economic benefits.